RustSight

The Need

Data scientists spend up to 60% of their time on the unglamorous but critical step that comes before AI/ML training: data validation and exploratory analysis. Python-based tools are slow for large datasets, while GUI applications lack automation capabilities.

Built with Rust

RustSight is a fast, safe, and extensible CLI tool that makes dataset analysis instant—even for massive CSV files. Written in Rust, it prioritizes performance and reliability.

Core Features

CSV Dataset Analysis

• ✅ Smart Column Detection: Automatically identifies numeric vs categorical columns
• ✅ Missing Value Analysis: Counts missing values per column with detailed reporting
• ✅ Streaming Processing: Handles large CSV files efficiently without loading entire datasets into memory
• ✅ Clean Reports: Generates readable _report.txt files for documentation
• ✅ Universal Compatibility: Works on any CSV file

File Analysis Mode

• 📊 Total byte count
• 🔍 UTF-8 validity detection
• 📝 Line and word counts (for text files)
• 🔢 Non-ASCII byte counting (for binaries)

Performance Advantage

Thanks to Rust's zero-cost abstractions and memory safety guarantees:

• 10-100x faster than equivalent Python implementations
• Memory efficient streaming for files of any size
• No runtime errors from type mismatches or null values
• Cross-platform compilation (Windows, macOS, Linux)

Real-World Usage

bash
# Analyze a financial dataset
cargo run -- csv stockdata.csv

# Analyze healthcare data
cargo run -- csv "CVD Dataset.csv"

# Inspect any file (text or binary)
cargo run -- analyze dataset.csv

What You Get

Column-wise Analysis:

• Data type detection
• Statistical summaries for numeric columns
• Category counts for categorical data
• Missing value percentages

Saved Reports:

• Automatic generation of filename_report.txt
• Clean, human-readable formatting
• Perfect for documentation and sharing

Tech Excellence

• Rust — Memory safety without garbage collection
• csv crate — Efficient, battle-tested CSV parsing
• Streaming architecture — Process files larger than RAM

Open Source

MIT Licensed. Contributions welcome!

"The unglamorous step before the magic happens" — Now blazing fast.